Authors - Arokiaraj S, Amudha T, Swamynathan R Abstract - Technology has become the driving force of progress and development all around the world. The recent development of Generative Artificial Intelligence has led to a revolution in the field of Education, Employment and Human Resources. Securing a dream job or building a suitable career is the goal of every student. Likewise finding the right candidate for the job is the goal of every employer. LLMs (Large Language Model) comes to the rescue, through dynamic question content creation for a selected topic and test the candidate for that set of skills. A candidate’s unique set of skills and abilities are understood and tested by the Generative AI where conventional methods fall short to meet this criterion. This paper proposes an automated question generation framework built using LangChain, LLM model -GPT Turbo 3.5 from OpenAI API and Streamlit application development tool. This application successfully tests the skills of the candidates by asking customized multiple-choice, true/false and open-ended questions based on their chosen topic, knowledge level, number of questions and time limit. Results indicate that this framework can create a challenging environment for the contenders thereby facilitating the interview process and selection of highly suitable candidates.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room EGOA, India
Authors - Shailaja Uke, Mohit Garg, Suyash Chandolikar, Swayam Chandak, Shriraj Nelekar Abstract - Chest X-rays are the most common tool for diagnosing various thoracic diseases. However, manual interpretation is time-consuming and prone to human error. This paper presents a deep learning approach for automated pathology detection in CXRs using the Customized DenseNet-121 model. The model performs binary classification to identify 14 pathologies, including cardiomegaly, pneumothorax, mass, and edema. To address class imbalance in medical imaging datasets, weight normalization is applied. Additionally, the visualization technique of Grad-CAM enhances interpretability by pointing out the most critical regions influencing the model's decisions, which helps healthcare practitioners assess. Toward further refinement of segmentation and improvement in precision of localization, we incorporate a customized U-Net model to enhance better delineation of regions of interest. Our model achieves an overall AUC of 87%, showing the highest accuracy. The customized U-Net integration improves seg-mentation performance, reducing localization error by 15%. This approach not only enhances diagnostic accuracy but also provides transparent decision-making, making it a valuable tool for medical professionals.
Authors - Aditya Gaura, Mandeep Kaur, Kimmi Verma, Monali Gulhane, Nitin Rakesh Abstract - This cutting-edge research paper introduces a paradigm shift in parking management, underpinned by an intricate network of technology and user-centric design. The system's hallmark feature is its advanced slot allocation mechanism. Users can make reservations via the mobile or web application, with the system autonomously assigning slots based on a holistic evaluation of user characteristics, such as vehicle type and duration of stay. Leveraging IoT integration, the system employs a sophisticated array of sensors and cameras to monitor parking slot occupancy in real-time, resulting in a fluid entry and exit process. The user experience is paramount in this system. It offers a tailored approach based on user type, streamlining the process for faculty, students, and visitors. Notably, the reduction in the time spent hunting for parking spots has the potential to mitigate the perennial issue of urban traffic congestion. This, in turn, aligns with environmental conservation efforts, as the system indirectly lowers emissions and the carbon footprint associated with circling for parking spaces. Moreover, this system's role as a data aggregator is invaluable. It collects and processes a wealth of data, offering parking operators unprecedented insights into daily usage patterns, peak periods, and favored slots. This data-driven approach empowers operators to make informed decisions about slot management, maintenance, and resource allocation.
Authors - Balwinder Kaur, Jaswinder Singh, Deepika Abstract - Nature-inspired optimization algorithms constitute a class of computational techniques that derive their underlying mechanisms from biological, ecological, and physical systems. By emulating processes such as evolutionary adaptation, collective swarm behavior, and decentralized decision-making, these algorithms offer robust solutions to complex optimization challenges across engineering and computational domains. Notable methodologies include Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization, each demonstrating efficacy in handling both single and multi-objective optimization problems, including those involving high-dimensional search spaces and non-linear constraints. Within the field of Automatic Speech Recognition (ASR), nature-inspired optimization techniques are instrumental in refining critical system components. Their application spans feature selection, acoustic model training, language model optimization, and efficient decoding strategies. By leveraging adaptive search mechanisms, these algorithms enhance model accuracy, reduce computational overhead, and improve generalization in ASR systems. This research study presents a systematic examination of nature-inspired optimization methods, focusing on their theoretical foundations and practical implementations in ASR. Furthermore, it critically evaluates existing challenges, such as sensitivity to hyperparameter tuning, computational scalability with large-scale datasets, and the absence of comprehensive convergence guarantees. Addressing these limitations is essential for advancing the applicability of nature-inspired optimization in next-generation speech recognition systems and related domains.
Authors - Anchal Saini, Nitin Kulshrestha Abstract - In the rapidly digitizing financial landscape, the ability to effectively use digital tools has become essential for financial well-being. This study examines the impact of digital competence on financial resilience within dual-income married couples, adopting a dyadic perspective. Drawing on the Actor-Partner Interdependence Moderation Model(APIMoM), it studies both actor and partner effects of digital competence. As well as the moderating role of each partner’s attitude toward FinTech. Data was collected from 107(214 individuals) working couples in Gurgaon, India. Covariance-Based Structural Equation Modeling using SmartPLS revealed that digital competence significantly influences both individuals' and their partners’ financial resilience. Moreover, attitudes toward FinTech were found to moderate these relationships, strengthening the positive effects of digital competence. Notably, the husband’s attitude had a stronger moderating impact on the wife’s resilience than vice versa, indicating potential gender-based dynamics. The study marks the importance of addressing both digital skills and relational attributes in aiding household financial resilience. Practical implications suggest that digital literacy programs should consider couple-based interventions that target both digital competence and attitude change.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room EGOA, India
Authors - Rasika Ransing, Kaushik Sakre, Neha Kudu, Shivam Shinde, Siddhi Talkar Abstract - The introduction of Automated Essay Scoring systems brought better assessment methods into education through standardized scoring systems that operate at scale while being time efficient. The current AES models function exclusively with English content while neglecting multilingual evaluation, particularly in the Hindi and Marathi languages. A multilingual AES framework has been developed using transformer models XLM-RoBERTa, MuRIL, DistilBERT, and mBERT for conducting context-based essay assessments throughout English, Hindi, and Marathi texts. Through multilingual embeddings combined with fine-tuned models, the system maintains cohesive and coherent, and argumentative quality in essays. The assessment by QWK and RMSE metrics demonstrates both high accuracy and reliability of the system. The highest performance emerged from XLM-RoBERTa and Google MuRIL at 0.78 QWK and 0.77 QWK, respectively.
Authors - Sarvesh Shinde, Tarun Kurakula, Venugopal Murugan, Tanmay Patil, Aparna Bannore Abstract - Network security is a difficult topic these days, with threats appearing quickly and everywhere. According to the study "Network Security Using Graph Embedding," connections are visible when jumbled network data is transformed into transparent graphs. First-order graphs have direct node links, while second-order graphs have nodes that share neighbours. DeepWalk, Node2Vec, and sense-making tools. Node2Vec, choice-based, tight groups or large network view, and modified random walks. DeepWalk is a straightforward, sequential structure mapping method. Both embeddings are feasible in terms of network layout. A graph as opposed to the outdated equal-link techniques, Attention Network, GAT, and anomaly hunt use attention tricks for important connections. fresh activity in the dataset, labeled data, normal versus odd markers, and real-time data. Strange spikes, unusual nodes, rules established, and threats identified. In continuous networks, not data crunch for kicks, quick catch, hackers, or weak spots.
Authors - Atharva Shirbhate, Jay Sutar, Om Tathed, Bhupal Shelke, Geeta S. Navale Abstract - The development of Artificial Intelligence (AI) has led to significant advancements across numerous domains, including finance, healthcare, and customer service. Recent progress, particularly in the field of Natural Language Processing (NLP), has been driven by the emergence of Large Language Models (LLMs). These models utilize transformer architectures and vast datasets to perform a wide range of tasks, such as language translation, text generation, and complex data analysis. As AI technology continues to evolve, it offers the potential to streamline decision-making processes, enhance data management, and provide personalized recommendations. This study focuses on leveraging AI to address specific challenges in the financial sector. The objective of this paper is twofold: firstly, to develop a system capable of recommending bonds based on user-specific requirements, thus aiding investors in making informed decisions; and secondly, to collect bond data from sellers and integrate it seamlessly into an existing database. Through a systematic review of recent AI advancements and prompt engineering techniques, this paper aims to provide insights into how these technologies can be harnessed to improve financial data integration and recommendation systems
Authors - Kumkum Saxena, Ayesha Nagdawala, Esha Nemani, Jatin Mawa, Mamta Gupta Abstract - Even in the modern days of digital era, reporting crimes like those of corruption or misconduct is still difficult owing to the fear of retaliation. Some traditional reporting mechanisms are available, but they often do not allow enough anonymity or security, preventing tipsters from reporting. Many whistleblowers face serious consequences, including job loss, legal action, or even physical threats, making them reluctant to report wrongdoing. SHADE is a blockchain-based solution that seeks to overcome these challenges by providing a decentralized and tamper-resistant medium for anonymous tip-offs. SHADE stands apart from conventional systems, which store centralized databases vulnerable to breach, providing full anonymity and data integrity with encryption. This paper explores SHADE’s architecture, which integrates blockchain for immutable data storage, cryptographic encryption for secure communication, and smart contracts for automated processing.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room EGOA, India
Authors - Suhas Bhise, Ketki Kshirsagar, Vivek Deshpande Abstract - In the context of Industrial Edge Computing, the growing deployment of IoT and mobile devices has resulted in an explosion of real-time, high-velocity time series data. This paper investigates statistical and machine learning approaches for time series forecasting in such environments, where latency, bandwidth, and computational efficiency are critical constraints. We evaluate traditional methods like Simple Moving Average (SMA), Holt-Winters Exponential Smoothing, and ARIMA, and contrast them with machine learning models such as Logistic Regression and XGBoost. Experiments conducted on the Microsoft Azure Predictive Maintenance dataset demonstrate that SMA and ARIMA offer comparable baseline accuracy, while XGBoost outperforms them in terms of forecast quality for multivariate series. We also explore the effectiveness of SMOTE for improving failure prediction using logistic regression. The findings suggest that lightweight models like XGBoost with lag feature engineering can be viable for forecasting in edge environments.
